class_docstr: A posterior based on GPyTorch's multi-variate Normal distributions.
docstr: The posterior variance.
docstr_tok: ["The","posterior","variance","."]
code: def variance(self) -> Tensor:
      r"""The posterior variance."""
      variance = self.distribution.variance
      if not self._is_mt:
          variance = variance.unsqueeze(-1)
      return variance
  
imports: from __future__ import annotations
  import warnings
  from contextlib import ExitStack
  from typing import Optional, Tuple, TYPE_CHECKING, Union
  import torch
  from botorch.exceptions.errors import BotorchTensorDimensionError
  from botorch.posteriors.base_samples import _reshape_base_samples_non_interleaved
  from botorch.posteriors.torch import TorchPosterior
  from gpytorch.distributions import MultitaskMultivariateNormal, MultivariateNormal
  from linear_operator import settings
  from linear_operator.operators import BlockDiagLinearOperator, DenseLinearOperator, LinearOperator, SumLinearOperator
  from torch import Tensor
  from torch.distributions import Normal
label_desc: Use image node_id 9 for calling the GPyTorchPosterior obj's underlying member method code with example usage: obj.variance() and returns: variance